Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline
- URL: http://arxiv.org/abs/2403.03265v1
- Date: Tue, 5 Mar 2024 19:03:56 GMT
- Title: Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline
- Authors: Lampis Alevizos, Martijn Dekker,
- Abstract summary: This paper explores the potential of integrating Artificial Intelligence (AI) into Cyber Threat Intelligence (CTI)
We provide a blueprint of an AI-enhanced CTI processing pipeline, and detail its components and functionalities.
We discuss ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Cyber threats continue to evolve in complexity, thereby traditional Cyber Threat Intelligence (CTI) methods struggle to keep pace. AI offers a potential solution, automating and enhancing various tasks, from data ingestion to resilience verification. This paper explores the potential of integrating Artificial Intelligence (AI) into CTI. We provide a blueprint of an AI-enhanced CTI processing pipeline, and detail its components and functionalities. The pipeline highlights the collaboration of AI and human expertise, which is necessary to produce timely and high-fidelity cyber threat intelligence. We also explore the automated generation of mitigation recommendations, harnessing AI's capabilities to provide real-time, contextual, and predictive insights. However, the integration of AI into CTI is not without challenges. Thereby, we discuss ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions. We address the need for data privacy, consent mechanisms, and the potential misuse of technology. Moreover, we highlights the importance of addressing biases both during CTI analysis and AI models warranting their transparency and interpretability. Lastly, our work points out future research directions such as the exploration of advanced AI models to augment cyber defences, and the human-AI collaboration optimization. Ultimately, the fusion of AI with CTI appears to hold significant potential in cybersecurity domain.
Related papers
- AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed Framework, Challenges, and Future Directions [10.16399860867284]
Artificial Intelligence (AI) techniques are transforming tactical operations by augmenting human decision-making capabilities.
This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach.
We propose a comprehensive framework that addresses the key components of AI-driven HAT.
arXiv Detail & Related papers (2024-10-28T15:05:16Z) - Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security [0.0]
This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity.
It develops an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks.
ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously.
arXiv Detail & Related papers (2024-05-09T18:15:12Z) - AI Potentiality and Awareness: A Position Paper from the Perspective of
Human-AI Teaming in Cybersecurity [18.324118502535775]
We argue that human-AI teaming is worthwhile in cybersecurity.
We emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise.
arXiv Detail & Related papers (2023-09-28T01:20:44Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Examining the Differential Risk from High-level Artificial Intelligence
and the Question of Control [0.0]
The extent and scope of future AI capabilities remain a key uncertainty.
There are concerns over the extent of integration and oversight of AI opaque decision processes.
This study presents a hierarchical complex systems framework to model AI risk and provide a template for alternative futures analysis.
arXiv Detail & Related papers (2022-11-06T15:46:02Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Security and Privacy for Artificial Intelligence: Opportunities and
Challenges [11.368470074697747]
In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques.
This challenge has motivated concerted research efforts into adversarial AI.
We present a holistic cyber security review that demonstrates adversarial attacks against AI applications.
arXiv Detail & Related papers (2021-02-09T06:06:13Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.